A current trend in the automotive industry is to introduce active safety systems for avoiding or mitigating collisions. One type of system, with a potentially large positive impact on accident statistics, is a forward collision avoidance system (FCAS). An FCAS uses sensors such as RADAR (RAdio Detection And Ranging), LIDAR (LIght Detection And Ranging) and cameras to monitor the region in front of the host vehicle. In the FCAS a tracking algorithm is used to estimate the state of the objects ahead and a decision algorithm uses the estimated states to determine any action, e.g. warning the driver, autonomous braking or steering.
The decision algorithms in automotive FCAS continuously evaluate the risk for a collision. However, in such an evaluation, only one obstacle at a time is considered. Usually there exists a mechanism to determine which of the potential collision objects that is most threatening in each time instant. Such an evaluation will not consider that a maneuver that avoids one obstacle may result in a collision with another. Each obstacle is only considered a threat in itself, and not in relation to other obstacles. This results in a decision making algorithm that, in multiple obstacle scenarios, may underestimate the collision threat.
Dealing with multiple obstacles can be very complicated since the number of possible scenarios grows exponentially with the number of obstacles. This may result in computationally demanding algorithms to determine the collision threat.